Social Media Content Category Analysis for Social Buzz
Favour Abiodun
Data Modelling Analyst
Data Visualizer
Data Analyst
Microsoft Excel
Microsoft Power BI
Tableau
Social Media Content Category Analysis for Social Buzz
Project Overview
This project analyzes the content categories of Social Buzz, a fictional social media company, to identify the top 5 categories with the largest aggregate popularity using Excel.
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Objectives
Audit of their big data practice.
Recommendations for a successful IPO.
Analysis of their content categories to highlight the top 5 categories with the largest aggregate popularity.
Project Approach
Data Understanding: Thoroughly understanding the data model and the business domain.
Data Cleaning: Cleaning the available datasets and designing an ideal dataset for the problem.
Data Modeling: Processing and modelling the data to answer business questions precisely.
Data Analysis: Using analytical techniques to uncover insights and create visualizations to describe these insights.
Visualization: Creating visual representations of the data insights.
Tools Used
Microsoft Excel
Findings
From the data, we identified 16 unique categories of posts. The top 5 categories by aggregate popularity are:
Animals: 74,965
Science: 71,168
Healthy Eating: 69,339
Technology: 68,738
Food: 66,676
Key Insights
Animal Content: High engagement with 1,897 reactions.
Seasonal Trends: January is the most common month for posts, aligning with post-holiday activity.
Health Conscious Users: Strong interest in healthy eating content.
